Resiliency Assessment and Enhancement of Intrinsic Fingerprinting

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Abstract

Intrinsic fingerprinting is a class of digital forensic technology that can detect traces left in digital multimedia data in order to reveal data processing history and determine data integrity. Many existing intrinsic fingerprinting schemes have implicitly assumed favorable operating conditions whose validity may become uncertain in reality. In order to establish intrinsic fingerprinting as a credible approach to digital multimedia authentication, it is important to understand and enhance its resiliency under unfavorable scenarios.
This dissertation addresses various resiliency aspects that can appear in a broad range of intrinsic fingerprints. The first aspect concerns intrinsic fingerprints that are designed to identify a particular component in the processing chain. Such fingerprints are potentially subject to changes due to input content variations and/or post-processing, and it is desirable to ensure their identifiability in such situations. Taking an image-based intrinsic fingerprinting technique for source camera model identification as a representative example, our investigations reveal that the fingerprints have a substantial dependency on image content. Such dependency limits the achievable identification accuracy, which is penalized by a mismatch between training and testing image content. To mitigate such a mismatch, we propose schemes to incorporate image content into training image selection and significantly improve the identification performance. We also consider the effect of post-processing against intrinsic fingerprinting, and study source camera identification based on imaging noise extracted from low-bit-rate compressed videos. While such compression reduces the fingerprint quality, we exploit different compression levels within the same video to achieve more efficient and accurate identification.
The second aspect of resiliency addresses anti-forensics, namely, adversarial actions that intentionally manipulate intrinsic fingerprints. We investigate the cost-effectiveness of anti-forensic operations that counteract color interpolation identification. Our analysis pinpoints the inherent vulnerabilities of color interpolation identification, and motivates countermeasures and refined anti-forensic strategies. We also study the anti-forensics of an emerging space-time localization technique for digital recordings based on electrical network frequency analysis. Detection schemes against anti-forensic operations are devised under a mathematical framework. For both problems, game-theoretic approaches are employed to characterize the interplay between forensic analysts and adversaries and to derive optimal strategies.
The third aspect regards the resilient and robust representation of intrinsic fingerprints for multiple forensic identification tasks. We propose to use the empirical frequency response as a generic type of intrinsic fingerprint that can facilitate the identification of various linear and shift-invariant (LSI) and non-LSI operations.